Lung cancer is the second most commonly diagnosed cancer in the United States, and other cancers frequently metastasize to the lung parenchyma as pulmonary nodules. Chest CT is the most sensitive diagnostic imaging modality for detecting lung mass or lung nodules. With widely accepted spiral CT and newly developed multislice CT techniques, the sensitivity in detecting pulmonary nodules has further improved.
Recently, spiral CT techniques have been applied to screening for lung cancer in high risk populations, and it has been shown to be useful in the early detection of lung cancers. See Henschke et al., Early Lung Cancer Action Project: overall design and findings from baseline screening, Lancet; vol. 213, pages 723-26 (1999), the disclosure of which is incorporated herein by reference. High resolution, three-dimensional (3D) imaging of a patient's thorax allows for the evaluation of small nodules at early stages. With follow-up scans, any changes in nodule number and nodule size can be assessed.
High resolution CT scans of the thorax generate a large amount of data—more than 250 images with a slice thickness of 1 mm. If CT scanning becomes widely practiced for lung cancer screening, an enormous demand will exist for physicians to review and interpret the scan images. To help alleviate such a burden in a high volume screening scenario, a computer system for automated nodule detection would be useful. Moreover, CAD systems can help improve the quality of a radiologist's performance in detecting pulmonary nodules.
Various CAD systems for pulmonary nodule detection using CT images have been proposed since the introduction of the first system by one of the inventors herein more than a decade ago (see Bae et al., Computer-aided detection of pulmonary nodules in CT images, Radiology 1991; 181 (P): 144, the entire disclosure of which is incorporated herein by reference). The majority of these CAD systems use detection algorithms based on two-dimensional (2D) morphologic features in each slice or serial 2D methods that examine connectivity of features in adjacent slices (see Reeves and Kostis, Computer-aided diagnosis for lung cancer, Radiol Clin North Am 2000; 38:497-509; and Armato et al., Automated detection of lung nodules in CT scans: preliminary results, Med Phys 2001; 28: 1552-61, the disclosures of both of which are incorporated herein by reference). Recently, two CAD methods have been reported that focus on 3D algorithms which take advantage of high resolution volumetric CT data obtained with multislice CT systems (see Qian et al., Knowledge-based automatic detection of multi-type lung nodules from multi-detector CT studies, SPIE 2002; 4684: 689-697; and Fan et al., Automatic detection of lung nodules from multi-slice low-dose CT images, SPIE 2001; 4332: 1828-1835, the disclosures of both of which are incorporated herein by reference).